I'd be really interested in feedback on the security model of client-side agents giving extension-bridge access, and taking questions on the implementation!
"Interesting architecture — embedding the agent inside the app context rather than outside it makes sense for session-aware tasks. One question: how do you handle output validation before the agent acts on the DOM? Client-side agents acting on live state without a certification layer seems like a reliability risk in production. We've been building ARU (aru-runtime.com) as a runtime certification layer for exactly this — curious if you've thought about that boundary."
The free testing LLM is Qwen hosted by Aliyun. Qwen and DeepSeek are the only ones I can afford to offer for free. It's just there to lower the try-out barrier; please DO NOT rely on it.
The library itself does NOT include any backend service. Your data only goes to the LLM api you configured.
I'm looking into a European testing endpoint. The legal and compliance requirements are quite hassle, and persuading my company to pay for that infrastructure is gonna be a tough sell.
I’ve been thinking about something like this. If it’s just a one line script import, how the heck are you trusting natural language to translate to commands for an arbitrary ui?
The only thing I can think of is you had the AI rewrite and embed selectors on the entire build file and work with that?
It uses a similiar process as `browser-use` but all in the web page. A script parses the live HTML, strips it down to its semantic essentials (HTML dehydration), and indexes every interactive element. That snapshot goes to the LLM, which returns actions referencing elements by index. The agent then simulates mouse/keyboard events on those elements via JS.
This works best on pages with proper semantic HTML and accessibility markup. You can test it right now on any page using the bookmarklet on the homepage (unless that page CSP blocks script injection of course).
Full transparency: I work at Alibaba and published this under Alibaba's open-source org. I sometines maintain it during work hours, so yes, Alibaba technically pays me for it. That said, this is my project — it's MIT-licensed, includes no backend service, and is open for anyone to audit.
The free testing LLM endpoint is hosted on Alibaba Cloud because I happen to have some company quota to spend, but it's not part of the library. Bring your own LLM and there is zero data transmission to Alibaba or anywhere else you haven't configured yourself.
I highly recommend using it with a local Ollama setup.
I'm particularly impressed by the bookmark "trick" to install it on a page. Despite having spent 15 years developing for the browser, I had somehow missed that feature of the bookmarks bar. But awesome UX for people to try out the tool. Congrats!
Bookmarklets are such an underrated feature. It's super convenient to inject and test scripts on any page. Seemed like the perfect low-friction entry point for people to try it out.
Spent some time on that UX because the concept is a bit hard to explain. Glad it worked!
I added in the system prompt that it should skip CAPTCHAs and hand control back to the user. Currently working on a proper human-in-the-loop feature. That's actually one of the key advantages of running the agent inside your own browser.
the CAPTCHA question points to a deeper issue: even before the CAPTCHA, most sites are already fingerprinting the browser that's running the agent. if the underlying browser leaks automation signals - navigator.webdriver, Canvas fingerprint deviations, WebGL anomalies - the session gets flagged or soft-blocked long before a CAPTCHA is even served.
the "inside your own browser" angle is actually the right intuition here. a real user's browser has built up a consistent fingerprint profile across sessions. the moment you run an agent in a context where those signals differ from that baseline, you're detectable.
curious whether you've run into this on sites with aggressive bot detection, or whether the use case has mostly been internal/enterprise apps where that's not a concern?
It supports any OpenAI-compatible API out of the box, so AWS Bedrock, LiteLLM, Ollama, etc. should all work. The free testing LLM is just there for a quick demo. Please bring your own LLM for long-time usage.
I think every successful Show HN post ends up with a "thought this was about X" or "didn't look up the name first?" comment. Consider it a win! I don't think anyone will mistake a tool for putty with your tool, but you might share a google search page with it.
I think page agent is good. I've never heard of putty's pageant. And I think it's better to distinguish it from general meaning of pageant (for beauty).
Not exactly the same but I'd also point to Paul Kinlan's FolioLM as a very interesting project in this space. A very nice browser extension,
> Collect and query content from tabs, bookmarks, and history - your AI research companion. FolioLM helps you collect sources from tabs, bookmarks, and history, then query and transform that content using AI.
This is highly experimental right now, but here are some quick links for anyone wanting to dig deeper:
- GitHub: https://github.com/alibaba/page-agent
- Live Demo (No sign-up): https://alibaba.github.io/page-agent/ (you can drag the bookmarklet from here to try it on other sites)
- Browser Extension: https://chromewebstore.google.com/detail/page-agent-ext/akld...
I'd be really interested in feedback on the security model of client-side agents giving extension-bridge access, and taking questions on the implementation!
I tried setting the LLM to "http://0.0.0.0:8080" and the extension crashed and now continues to crash at startup.
"Interesting architecture — embedding the agent inside the app context rather than outside it makes sense for session-aware tasks. One question: how do you handle output validation before the agent acts on the DOM? Client-side agents acting on live state without a certification layer seems like a reliability risk in production. We've been building ARU (aru-runtime.com) as a runtime certification layer for exactly this — curious if you've thought about that boundary."
> Data processed via servers in Mainland China
Appreciate the transparency, but maybe you could add some European (preferably) alternatives ?
Please use your own LLM api instead!
The free testing LLM is Qwen hosted by Aliyun. Qwen and DeepSeek are the only ones I can afford to offer for free. It's just there to lower the try-out barrier; please DO NOT rely on it.
The library itself does NOT include any backend service. Your data only goes to the LLM api you configured.
I tested it on local Ollama models it works fine.
I'm looking into a European testing endpoint. The legal and compliance requirements are quite hassle, and persuading my company to pay for that infrastructure is gonna be a tough sell.
I’ve been thinking about something like this. If it’s just a one line script import, how the heck are you trusting natural language to translate to commands for an arbitrary ui?
The only thing I can think of is you had the AI rewrite and embed selectors on the entire build file and work with that?
Everything happens at runtime, on the HTML level.
It uses a similiar process as `browser-use` but all in the web page. A script parses the live HTML, strips it down to its semantic essentials (HTML dehydration), and indexes every interactive element. That snapshot goes to the LLM, which returns actions referencing elements by index. The agent then simulates mouse/keyboard events on those elements via JS.
This works best on pages with proper semantic HTML and accessibility markup. You can test it right now on any page using the bookmarklet on the homepage (unless that page CSP blocks script injection of course).
Firefox support?
Is this Affiliated with the Chinese company Alibaba? Any chance data goes there too?
Full transparency: I work at Alibaba and published this under Alibaba's open-source org. I sometines maintain it during work hours, so yes, Alibaba technically pays me for it. That said, this is my project — it's MIT-licensed, includes no backend service, and is open for anyone to audit.
The free testing LLM endpoint is hosted on Alibaba Cloud because I happen to have some company quota to spend, but it's not part of the library. Bring your own LLM and there is zero data transmission to Alibaba or anywhere else you haven't configured yourself.
I highly recommend using it with a local Ollama setup.
Very cool!
I'm particularly impressed by the bookmark "trick" to install it on a page. Despite having spent 15 years developing for the browser, I had somehow missed that feature of the bookmarks bar. But awesome UX for people to try out the tool. Congrats!
Thanks!
Bookmarklets are such an underrated feature. It's super convenient to inject and test scripts on any page. Seemed like the perfect low-friction entry point for people to try it out.
Spent some time on that UX because the concept is a bit hard to explain. Glad it worked!
Curious - how does it perform with captchas and other "are you human" stuff on the web?
I added in the system prompt that it should skip CAPTCHAs and hand control back to the user. Currently working on a proper human-in-the-loop feature. That's actually one of the key advantages of running the agent inside your own browser.
the CAPTCHA question points to a deeper issue: even before the CAPTCHA, most sites are already fingerprinting the browser that's running the agent. if the underlying browser leaks automation signals - navigator.webdriver, Canvas fingerprint deviations, WebGL anomalies - the session gets flagged or soft-blocked long before a CAPTCHA is even served.
the "inside your own browser" angle is actually the right intuition here. a real user's browser has built up a consistent fingerprint profile across sessions. the moment you run an agent in a context where those signals differ from that baseline, you're detectable. curious whether you've run into this on sites with aggressive bot detection, or whether the use case has mostly been internal/enterprise apps where that's not a concern?
Makes sense.
For curiosity's sake, have you had it try to attempt captchas?
If so, what were the results?
I haven’t. I don’t think it will work well.
I use a text-based approach. Captchas like “crossroad” usually need a screenshot, a visual model and coordinate-based mouse events.
Looks cool! Are you open to adding AWS Bedrock or LiteLLM support?
Thanks!
It supports any OpenAI-compatible API out of the box, so AWS Bedrock, LiteLLM, Ollama, etc. should all work. The free testing LLM is just there for a quick demo. Please bring your own LLM for long-time usage.
Confusing name because of the existence of pageant, the putty agent.
Darn. Pageant would've been a nice name though. Maybe `page-agent.js` is more relevant in web dev community.
I think every successful Show HN post ends up with a "thought this was about X" or "didn't look up the name first?" comment. Consider it a win! I don't think anyone will mistake a tool for putty with your tool, but you might share a google search page with it.
I think page agent is good. I've never heard of putty's pageant. And I think it's better to distinguish it from general meaning of pageant (for beauty).
Thanks!
Came here to say missed opportunity to call it "PAgent". Rolls off the tongue better than Page Agent.
I'm 2 years too late for that one...
Does it support long-click / click-and-drag?
Not yet. Currently focused on the more common interaction patterns. PRs welcome though!
Gotcha. Still very cool! Congrats on the release.
Thanks!
Not exactly the same but I'd also point to Paul Kinlan's FolioLM as a very interesting project in this space. A very nice browser extension,
> Collect and query content from tabs, bookmarks, and history - your AI research companion. FolioLM helps you collect sources from tabs, bookmarks, and history, then query and transform that content using AI.
https://github.com/PaulKinlan/NotebookLM-Chrome https://chromewebstore.google.com/detail/foliolm/eeejhgacmlh...
I've been trying to arrive to something like this with my own sidepanel extension called Klue but its more of a user notes + web page context approach. Nice to see another take on this! https://chromewebstore.google.com/detail/cackjmmgcmnkjnffabk...
Thanks for sharing! We need more projects like this in the JS ecosystem.